Update app.py

app.py

@@ -265,6 +265,7 @@ def preprocess_collection(collection, pixel_cloud_threshold):
         return collection.map(mask_cloudy_pixels)
     return collection
 
+# Process single geometry
 # Process single geometry
 def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, custom_formula, original_lat_col, original_lon_col, kernel_size=None, include_boundary=None, user_scale=None, pixel_cloud_threshold=0):
     if shape_type.lower() == "point":
@@ -290,20 +291,18 @@ def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selec
                 roi = roi.buffer(-30).bounds()
         except ValueError:
             return None
-    
+
     # Filter collection by date and area first
-    # Apply spatial filtering
     collection = ee.ImageCollection(dataset_id) \
         .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
         .filterBounds(roi)
-    
+
     # Apply cloud filtering if applicable
     if pixel_cloud_threshold > 0:
         collection = preprocess_collection(collection, pixel_cloud_threshold)
         st.write(f"After cloud masking: {collection.size().getInfo()} images")
 
-    st.write(f"After initial filtering: {collection.size().getInfo()} images")
-    
+    # Apply temporal aggregation
     if aggregation_period.lower() == 'custom (start date to end date)':
         collection = aggregate_data_custom(collection)
     elif aggregation_period.lower() == 'daily':
@@ -314,7 +313,8 @@ def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selec
         collection = aggregate_data_monthly(collection, start_date_str, end_date_str)
     elif aggregation_period.lower() == 'yearly':
         collection = aggregate_data_yearly(collection)
-    
+
+    # Process the filtered collection
     image_list = collection.toList(collection.size())
     processed_weeks = set()
     aggregated_results = []
@@ -345,7 +345,7 @@ def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selec
             timestamp = image.get('year')
             period_label = 'Year'
             date = ee.Date(timestamp).format('YYYY').getInfo()
-        
+
         index_image = calculate_custom_formula(image, roi, selected_bands, custom_formula, reducer_choice, dataset_id, user_scale=user_scale)
         try:
             index_value = index_image.reduceRegion(
@@ -379,7 +379,7 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
     progress_text = st.empty()
     start_time = time.time()
 
-    # Fetch the original collection size
+    # Log the original collection size for debugging
     raw_collection = ee.ImageCollection(dataset_id) \
         .filterDate(ee.Date(start_date_str), ee.Date(end_date_str))
     st.write(f"Original Collection Size: {raw_collection.size().getInfo()}")